In this project we will try to find an optimal location for a restaurant. Specifically, this report will be targeted to stakeholders interested in opening an restaurant in Boston, Massachusetts.
Since there are lots of restaurants in Massachusetts we will try to detect locations that are not already crowded with restaurants. We are also particularly interested in areas with low crime rate and areas that have reasonable rent.
We will use K-means clustering to segment and cluster the venues in each neighborhoods.
To cater this problem we will need the following data sets data set which are freely available.
Crimes in Boston from Kaggle which contains the data of all the crimes occured in different neighborhoods in boston in recent times.
Boston Rental Data from Zillow which is real estate website containing most up to date rental data in Boston Neighborhoods.
Boston Police Districts from Boston Police Department website. We need this because we need to encode District Abbrevations into Boston Neighborhoods
import numpy as np # library to handle data in a vectorized manner
import pandas as pd # library for data analsysis
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
import json # library to handle JSON files
from geopy.geocoders import Nominatim # convert an address into latitude and longitude values
import requests # library to handle requests
from pandas.io.json import json_normalize # tranform JSON file into a pandas dataframe
# Matplotlib and associated plotting modules
import matplotlib.cm as cm
import matplotlib.colors as colors
import matplotlib.pyplot as plt
import seaborn as sns
# import k-means from clustering stage
from sklearn.cluster import KMeans
import folium # map rendering library
from zipfile import ZipFile
import requests
from bs4 import BeautifulSoup
print('Libraries imported.')
Libraries imported.
#download and upload data manually
data1 = pd.read_csv('crime.csv')
/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (6) have mixed types. Specify dtype option on import or set low_memory=False. interactivity=interactivity, compiler=compiler, result=result)
def DistrictData():
url = 'https://bpdnews.com/districts'
source = requests.get(url).text
soup = BeautifulSoup(source,'lxml')
links = soup.find_all('div', attrs={'class': 'sqs-block-content'})
h2s = soup.find_all('h2')
def getCodes(h2s):
container = []
i = 0
for h2 in h2s:
i = i + 1
line = h2.find('strong').text
if i == 1:
pass
elif i == 2:
code = line.split(' ')
container.append(code[1].replace('-',''))
container.append(code[3].replace('-',''))
else:
code = line.split(' ')
container.append(code[1].replace('-',''))
return container
def getNames(h2s):
container = []
i = 0
for h2 in h2s:
i = i + 1
x = h2.text.split('-')
if i == 1:
pass
elif i == 2:
item = x[2][2:]
names = item.split(' ')
container.append(names[0])
container.append(names[2])
elif i == 7 or i == 9 or i == 11 or i == 12:
name = x[1][2:]
container.append(name)
else:
name = x[1][1:]
container.append(name)
return container
Districts = getNames(h2s)
Codes = getCodes(h2s)
#data = pd.DataFrame({'Code' : Codes, 'Neighborhood' : Districts})
return Districts, Codes
l2, l1 = DistrictData()
print(l1)
print(l2)
['A1', 'A15', 'A7', 'B2', 'B3', 'C6', 'C11', 'D4', 'D14', 'E5', 'E13', 'E18'] ['Downtown', 'Charlestown', 'East Boston', 'Roxbury', 'Mattapan', 'South Boston', 'Dorchester', 'South End', 'Brighton', 'West Roxbury', 'Jamaica Plain', 'Hyde Park']
data1.replace(l1,l2,inplace=True)
data1.head()
| INCIDENT_NUMBER | OFFENSE_CODE | OFFENSE_CODE_GROUP | OFFENSE_DESCRIPTION | DISTRICT | REPORTING_AREA | SHOOTING | OCCURRED_ON_DATE | YEAR | MONTH | DAY_OF_WEEK | HOUR | UCR_PART | STREET | Lat | Long | Location | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | I182080058 | 2403 | Disorderly Conduct | DISTURBING THE PEACE | Hyde Park | 495 | NaN | 10/3/2018 20:13 | 2018 | 10 | Wednesday | 20 | Part Two | ARLINGTON ST | 42.262608 | -71.121186 | (42.26260773, -71.12118637) |
| 1 | I182080053 | 3201 | Property Lost | PROPERTY - LOST | Brighton | 795 | NaN | 8/30/2018 20:00 | 2018 | 8 | Thursday | 20 | Part Three | ALLSTON ST | 42.352111 | -71.135311 | (42.35211146, -71.13531147) |
| 2 | I182080052 | 2647 | Other | THREATS TO DO BODILY HARM | Roxbury | 329 | NaN | 10/3/2018 19:20 | 2018 | 10 | Wednesday | 19 | Part Two | DEVON ST | 42.308126 | -71.076930 | (42.30812619, -71.07692974) |
| 3 | I182080051 | 413 | Aggravated Assault | ASSAULT - AGGRAVATED - BATTERY | Downtown | 92 | NaN | 10/3/2018 20:00 | 2018 | 10 | Wednesday | 20 | Part One | CAMBRIDGE ST | 42.359454 | -71.059648 | (42.35945371, -71.05964817) |
| 4 | I182080050 | 3122 | Aircraft | AIRCRAFT INCIDENTS | East Boston | 36 | NaN | 10/3/2018 20:49 | 2018 | 10 | Wednesday | 20 | Part Three | PRESCOTT ST | 42.375258 | -71.024663 | (42.37525782, -71.02466343) |
crime_count = data1['DISTRICT'].value_counts()
a = crime_count.index
b = crime_count.values
crime_count = pd.DataFrame({'Neighborhood': a, 'Total' : b})
plot = crime_count.plot(kind = 'bar',x='Neighborhood',y='Total', figsize = (10,10))
fig = plot.get_figure()
fig.savefig("output.png")
top10offense = data1['OFFENSE_CODE_GROUP'].value_counts()[:10,]
plt.figure(figsize=(10,5))
sns.barplot(top10offense.index, top10offense.values, alpha=0.8)
plt.title('Top 10 offenses')
plt.ylabel('Number of Occurrences', fontsize=12)
plt.xlabel('Offense', fontsize=12)
plt.xticks(
rotation=45,
horizontalalignment='right',
fontweight='light',
#fontsize='x-large'
)
plt.savefig('top10offense.png')
plt.show()
We can see that Larceny, otherwise known as theft, is very prolifent in Boston
data2 = data1[data1['OFFENSE_CODE_GROUP'] == 'Larceny']
data2 = data2[data2['OFFENSE_DESCRIPTION']=='LARCENY SHOPLIFTING']
data2 = data2[data2['YEAR']==2018].reset_index(drop=True)
data2 = data2.iloc[0:500,:]
print(data2.shape)
data2.head()
(500, 17)
| INCIDENT_NUMBER | OFFENSE_CODE | OFFENSE_CODE_GROUP | OFFENSE_DESCRIPTION | DISTRICT | REPORTING_AREA | SHOOTING | OCCURRED_ON_DATE | YEAR | MONTH | DAY_OF_WEEK | HOUR | UCR_PART | STREET | Lat | Long | Location | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | I182080029 | 613 | Larceny | LARCENY SHOPLIFTING | South End | 146 | NaN | 10/3/2018 19:09 | 2018 | 10 | Wednesday | 19 | Part One | HUNTINGTON AVE | 42.347972 | -71.078496 | (42.34797240, -71.07849582) |
| 1 | I182080003 | 613 | Larceny | LARCENY SHOPLIFTING | South Boston | 214 | NaN | 10/3/2018 17:45 | 2018 | 10 | Wednesday | 17 | Part One | W BROADWAY | 42.336112 | -71.046654 | (42.33611200, -71.04665434) |
| 2 | I182079973 | 613 | Larceny | LARCENY SHOPLIFTING | Downtown | 94 | NaN | 10/3/2018 13:40 | 2018 | 10 | Wednesday | 13 | Part One | FANEUIL HALL MARKETPLACE | 42.359980 | -71.054741 | (42.35997967, -71.05474135) |
| 3 | I182079942 | 613 | Larceny | LARCENY SHOPLIFTING | Downtown | 103 | NaN | 10/3/2018 14:47 | 2018 | 10 | Wednesday | 14 | Part One | WASHINGTON ST | 42.356801 | -71.058930 | (42.35680075, -71.05893025) |
| 4 | I182079899 | 613 | Larceny | LARCENY SHOPLIFTING | Mattapan | 427 | NaN | 10/3/2018 13:38 | 2018 | 10 | Wednesday | 13 | Part One | BLUE HILL AVE | 42.274701 | -71.093441 | (42.27470119, -71.09344053) |
data3 = data2[['DISTRICT','STREET','Lat','Long']].dropna()
#data3.rename(columns={'OFFENSE_CODE_GROUP': 'OFFENSE'}, inplace = True)
print(data3.shape)
data3.head()
(489, 4)
| DISTRICT | STREET | Lat | Long | |
|---|---|---|---|---|
| 0 | South End | HUNTINGTON AVE | 42.347972 | -71.078496 |
| 1 | South Boston | W BROADWAY | 42.336112 | -71.046654 |
| 2 | Downtown | FANEUIL HALL MARKETPLACE | 42.359980 | -71.054741 |
| 3 | Downtown | WASHINGTON ST | 42.356801 | -71.058930 |
| 4 | Mattapan | BLUE HILL AVE | 42.274701 | -71.093441 |
address = 'Boston, MA'
geolocator = Nominatim(user_agent="ny_explorer")
location = geolocator.geocode(address)
latitude = location.latitude
longitude = location.longitude
print('The geograpical coordinate of Boston are {}, {}.'.format(latitude, longitude))
The geograpical coordinate of Boston are 42.3602534, -71.0582912.
boston_map = folium.Map(location=[latitude, longitude], zoom_start=12)
from folium import plugins
# let's start again with a clean copy of the map of San Francisco
boston_map = folium.Map(location = [latitude, longitude], zoom_start = 12)
# instantiate a mark cluster object for the incidents in the dataframe
incidents = plugins.MarkerCluster().add_to(boston_map)
# loop through the dataframe and add each data point to the mark cluster
for lat, lng, label, in zip(data3.Lat, data3.Long, data3['STREET']):
folium.Marker(
location=[lat, lng],
icon=None,
popup=label,
).add_to(incidents)
# display map
boston_map.save('fig1.html')
boston_map
We can observe that most of the offenses are centered aound the South End area therefore, this region would not be ideal for openning a new restaurant
housing_data = pd.read_csv('http://files.zillowstatic.com/research/public/Neighborhood/Neighborhood_ZriPerSqft_AllHomes.csv')
housing_data.head()
| RegionID | RegionName | City | State | Metro | CountyName | SizeRank | 2010-11 | 2010-12 | 2011-01 | 2011-02 | 2011-03 | 2011-04 | 2011-05 | 2011-06 | 2011-07 | 2011-08 | 2011-09 | 2011-10 | 2011-11 | 2011-12 | 2012-01 | 2012-02 | 2012-03 | 2012-04 | 2012-05 | 2012-06 | 2012-07 | 2012-08 | 2012-09 | 2012-10 | 2012-11 | 2012-12 | 2013-01 | 2013-02 | 2013-03 | 2013-04 | 2013-05 | 2013-06 | 2013-07 | 2013-08 | 2013-09 | 2013-10 | 2013-11 | 2013-12 | 2014-01 | 2014-02 | 2014-03 | 2014-04 | 2014-05 | 2014-06 | 2014-07 | 2014-08 | 2014-09 | 2014-10 | 2014-11 | 2014-12 | 2015-01 | 2015-02 | 2015-03 | 2015-04 | 2015-05 | 2015-06 | 2015-07 | 2015-08 | 2015-09 | 2015-10 | 2015-11 | 2015-12 | 2016-01 | 2016-02 | 2016-03 | 2016-04 | 2016-05 | 2016-06 | 2016-07 | 2016-08 | 2016-09 | 2016-10 | 2016-11 | 2016-12 | 2017-01 | 2017-02 | 2017-03 | 2017-04 | 2017-05 | 2017-06 | 2017-07 | 2017-08 | 2017-09 | 2017-10 | 2017-11 | 2017-12 | 2018-01 | 2018-02 | 2018-03 | 2018-04 | 2018-05 | 2018-06 | 2018-07 | 2018-08 | 2018-09 | 2018-10 | 2018-11 | 2018-12 | 2019-01 | 2019-02 | 2019-03 | 2019-04 | 2019-05 | 2019-06 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 274772 | Northeast Dallas | Dallas | TX | Dallas-Fort Worth-Arlington | Dallas County | 1 | 0.844 | 0.850 | 0.858 | 0.862 | 0.862 | 0.858 | 0.856 | 0.858 | 0.860 | 0.862 | 0.864 | 0.862 | 0.862 | 0.858 | 0.854 | 0.854 | 0.856 | 0.860 | 0.864 | 0.872 | 0.882 | 0.892 | 0.900 | 0.904 | 0.904 | 0.904 | 0.904 | 0.908 | 0.912 | 0.918 | 0.926 | 0.934 | 0.942 | 0.944 | 0.952 | 0.958 | 0.966 | 0.968 | 0.972 | 0.976 | 0.978 | 0.980 | 0.982 | 0.986 | 0.994 | 1.010 | 1.024 | 1.032 | 1.036 | 1.036 | 1.036 | 1.036 | 1.038 | 1.042 | 1.052 | 1.068 | 1.084 | 1.098 | 1.106 | 1.110 | 1.112 | 1.110 | 1.112 | 1.110 | 1.114 | 1.120 | 1.132 | 1.144 | 1.152 | 1.160 | 1.164 | 1.168 | 1.170 | 1.168 | 1.164 | 1.162 | 1.160 | 1.162 | 1.168 | 1.176 | 1.188 | 1.196 | 1.202 | 1.202 | 1.200 | 1.196 | 1.194 | 1.192 | 1.190 | 1.188 | 1.186 | 1.186 | 1.186 | 1.186 | 1.186 | 1.188 | 1.196 | 1.208 | 1.216 | 1.220 | 1.220 | 1.222 | 1.224 | 1.230 |
| 1 | 112345 | Maryvale | Phoenix | AZ | Phoenix-Mesa-Scottsdale | Maricopa County | 2 | 0.672 | 0.670 | 0.666 | 0.664 | 0.664 | 0.662 | 0.660 | 0.658 | 0.656 | 0.656 | 0.654 | 0.652 | 0.650 | 0.646 | 0.642 | 0.638 | 0.636 | 0.636 | 0.640 | 0.646 | 0.650 | 0.650 | 0.650 | 0.646 | 0.646 | 0.646 | 0.648 | 0.646 | 0.648 | 0.650 | 0.652 | 0.654 | 0.656 | 0.658 | 0.660 | 0.658 | 0.658 | 0.660 | 0.662 | 0.662 | 0.660 | 0.660 | 0.662 | 0.666 | 0.672 | 0.676 | 0.678 | 0.680 | 0.682 | 0.688 | 0.692 | 0.696 | 0.698 | 0.702 | 0.704 | 0.710 | 0.718 | 0.726 | 0.728 | 0.724 | 0.718 | 0.714 | 0.716 | 0.726 | 0.738 | 0.752 | 0.764 | 0.780 | 0.790 | 0.790 | 0.788 | 0.786 | 0.784 | 0.778 | 0.774 | 0.768 | 0.766 | 0.768 | 0.776 | 0.788 | 0.802 | 0.812 | 0.820 | 0.824 | 0.826 | 0.826 | 0.826 | 0.826 | 0.832 | 0.836 | 0.838 | 0.842 | 0.844 | 0.846 | 0.848 | 0.850 | 0.862 | 0.876 | 0.888 | 0.896 | 0.898 | 0.900 | 0.900 | 0.904 |
| 2 | 192689 | Paradise | Las Vegas | NV | Las Vegas-Henderson-Paradise | Clark County | 3 | 0.758 | 0.756 | 0.754 | 0.752 | 0.750 | 0.748 | 0.746 | 0.744 | 0.744 | 0.746 | 0.744 | 0.742 | 0.740 | 0.736 | 0.734 | 0.732 | 0.728 | 0.728 | 0.726 | 0.728 | 0.730 | 0.730 | 0.728 | 0.726 | 0.724 | 0.724 | 0.722 | 0.722 | 0.722 | 0.724 | 0.724 | 0.726 | 0.726 | 0.728 | 0.730 | 0.732 | 0.732 | 0.730 | 0.728 | 0.728 | 0.728 | 0.728 | 0.730 | 0.730 | 0.734 | 0.736 | 0.740 | 0.742 | 0.742 | 0.742 | 0.742 | 0.742 | 0.744 | 0.748 | 0.750 | 0.754 | 0.760 | 0.764 | 0.766 | 0.766 | 0.764 | 0.762 | 0.764 | 0.768 | 0.772 | 0.776 | 0.778 | 0.782 | 0.782 | 0.786 | 0.786 | 0.786 | 0.786 | 0.786 | 0.786 | 0.786 | 0.788 | 0.790 | 0.796 | 0.800 | 0.806 | 0.810 | 0.814 | 0.820 | 0.824 | 0.828 | 0.830 | 0.830 | 0.832 | 0.834 | 0.836 | 0.838 | 0.838 | 0.838 | 0.840 | 0.842 | 0.854 | 0.870 | 0.884 | 0.888 | 0.892 | 0.896 | 0.902 | 0.912 |
| 3 | 270958 | Upper West Side | New York | NY | New York-Newark-Jersey City | New York County | 4 | 4.456 | 4.470 | 4.466 | 4.450 | 4.392 | 4.302 | 4.224 | 4.160 | 4.138 | 4.098 | 4.092 | 4.046 | 4.014 | 3.986 | 3.990 | 4.020 | 4.044 | 4.056 | 4.068 | 4.018 | 4.038 | 4.062 | 4.118 | 4.156 | 4.196 | 4.210 | 4.150 | 4.074 | 4.058 | 4.098 | 4.150 | 4.218 | 4.242 | 4.208 | 4.164 | 4.170 | 4.234 | 4.326 | 4.404 | 4.476 | 4.522 | 4.542 | 4.536 | 4.528 | 4.532 | 4.556 | 4.604 | 4.688 | 4.782 | 4.836 | 4.880 | 4.912 | 4.938 | 4.958 | 4.974 | 4.994 | 5.020 | 5.062 | 5.096 | 5.102 | 5.080 | 5.058 | 5.042 | 5.042 | 5.046 | 5.070 | 5.082 | 5.086 | 5.066 | 5.050 | 5.036 | 5.022 | 5.016 | 5.012 | 5.012 | 5.012 | 5.014 | 5.014 | 5.012 | 5.006 | 5.006 | 5.006 | 5.010 | 5.002 | 4.986 | 4.958 | 4.942 | 4.934 | 4.934 | 4.930 | 4.920 | 4.908 | 4.902 | 4.900 | 4.900 | 4.912 | 4.944 | 4.978 | 5.008 | 5.026 | 5.048 | 5.076 | 5.152 | 5.234 |
| 4 | 118208 | South Los Angeles | Los Angeles | CA | Los Angeles-Long Beach-Anaheim | Los Angeles County | 5 | 1.416 | 1.412 | 1.410 | 1.414 | 1.414 | 1.410 | 1.406 | 1.400 | 1.404 | 1.404 | 1.408 | 1.412 | 1.416 | 1.412 | 1.402 | 1.394 | 1.394 | 1.396 | 1.400 | 1.394 | 1.386 | 1.370 | 1.360 | 1.356 | 1.356 | 1.364 | 1.380 | 1.400 | 1.416 | 1.426 | 1.438 | 1.442 | 1.440 | 1.434 | 1.432 | 1.442 | 1.456 | 1.464 | 1.462 | 1.456 | 1.446 | 1.452 | 1.454 | 1.456 | 1.456 | 1.466 | 1.496 | 1.522 | 1.556 | 1.580 | 1.604 | 1.620 | 1.636 | 1.642 | 1.644 | 1.646 | 1.654 | 1.662 | 1.666 | 1.672 | 1.672 | 1.682 | 1.696 | 1.716 | 1.724 | 1.734 | 1.738 | 1.746 | 1.750 | 1.748 | 1.752 | 1.760 | 1.778 | 1.804 | 1.828 | 1.850 | 1.868 | 1.886 | 1.898 | 1.910 | 1.924 | 1.940 | 1.954 | 1.962 | 1.964 | 1.962 | 1.964 | 1.972 | 1.984 | 1.990 | 1.990 | 1.984 | 1.988 | 1.994 | 1.998 | 2.008 | 2.024 | 2.050 | 2.070 | 2.080 | 2.082 | 2.082 | 2.082 | 2.082 |
rents = housing_data.loc[(housing_data['State'] == 'MA') & (housing_data['City']=='Boston'),['RegionName','2019-06']].reset_index(drop=True)
rents.rename(columns={'RegionName':'Neighborhood','2019-06':'Median price per sqft'}, inplace = True)
temp = rents.iloc[1:]
temp.replace('North Dorchester','Dorchester',inplace = True)
temp1 = temp.append({'Neighborhood':'Seaport District','Median price per sqft':3.577},ignore_index=True)
temp2 = temp1.append({'Neighborhood':'Longwood Medical Area','Median price per sqft':2.534},ignore_index=True)
rents = temp2
/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py:4042: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy method=method)
#get the coordinates
def getCoordinates(address):
geolocator = Nominatim(user_agent="ny_explorer")
location = geolocator.geocode(address)
latitude = location.latitude
longitude = location.longitude
return latitude,longitude
neighborhoods = rents['Neighborhood']
Latitudes = []
Longitudes = []
for n in neighborhoods:
address = n + ', MA, USA'
lat,lon = getCoordinates(address)
#print(n + ' ' +str(lat)+','+str(lon))
Latitudes.append(lat)
Longitudes.append(lon)
rents['Lat'] = Latitudes
rents['Lon'] = Longitudes
rents.replace(['Seaport District','Longwood Medical Area'],['South Boston Waterfront','Longwood'],inplace= True)
rents
| Neighborhood | Median price per sqft | Lat | Lon | |
|---|---|---|---|---|
| 0 | Roxbury | 1.914 | 42.324843 | -71.095016 |
| 1 | Jamaica Plain | 2.360 | 42.309820 | -71.120330 |
| 2 | East Boston | 2.442 | 42.375097 | -71.039217 |
| 3 | Brighton | 2.896 | 42.350097 | -71.156442 |
| 4 | Mattapan | 1.896 | 42.272321 | -71.086995 |
| 5 | South Boston | 2.994 | 42.333431 | -71.049495 |
| 6 | Hyde Park | 1.984 | 42.255654 | -71.124496 |
| 7 | West Roxbury | 1.940 | 42.279265 | -71.149497 |
| 8 | South End | 3.670 | 42.095928 | -72.582866 |
| 9 | Roslindale | 1.992 | 42.291209 | -71.124497 |
| 10 | Dorchester | 2.286 | 42.297320 | -71.074495 |
| 11 | Allston | 3.018 | 42.355434 | -71.132127 |
| 12 | Back Bay | 4.088 | 42.350707 | -71.079730 |
| 13 | Fenway | 4.216 | 42.343451 | -71.097716 |
| 14 | Charlestown | 2.838 | 42.377875 | -71.061996 |
| 15 | Kenmore | 4.084 | 39.692611 | -75.867163 |
| 16 | Mission Hill | 2.524 | 42.332926 | -71.103214 |
| 17 | North End | 3.734 | 42.365097 | -71.054495 |
| 18 | Beacon Hill | 4.260 | 42.359820 | -71.066162 |
| 19 | Chinatown | 4.002 | 42.352217 | -71.062607 |
| 20 | West End | 3.290 | 42.363919 | -71.063899 |
| 21 | Downtown | 3.592 | 42.263069 | -71.802002 |
| 22 | Bay Village | 3.984 | 42.350011 | -71.066948 |
| 23 | South Boston Waterfront | 3.577 | 42.348765 | -71.043988 |
| 24 | Longwood | 2.534 | 42.336168 | -71.099527 |
!wget --quiet http://bostonopendata-boston.opendata.arcgis.com/datasets/3525b0ee6e6b427f9aab5d0a1d0a1a28_0.geojson -O Boston.json
print('json file downloaded')
json file downloaded
boston_geo = r'Boston.json'
with open('Boston.json') as json_data:
boston_data = json.load(json_data)
del(boston_data['features'][-1])
boston_map = folium.Map(location=[42.3602534, -71.0582912], zoom_start=12)
boston_map.choropleth(
geo_data=boston_data,
data=rents,
columns=['Neighborhood', 'Median price per sqft'],
key_on='feature.properties.Name',
fill_color='YlOrRd',
fill_opacity=0.7,
line_opacity=0.2,
legend_name='Housing Prices per sqft',
reset=True
)
boston_map
/usr/local/lib/python3.6/dist-packages/folium/folium.py:426: FutureWarning: The choropleth method has been deprecated. Instead use the new Choropleth class, which has the same arguments. See the example notebook 'GeoJSON_and_choropleth' for how to do this. FutureWarning
As shown in the map, we observe that the regions in the southeren reghion have very high rent. Thus, these regions should not be chosen if there are budget constraints.
CLIENT_ID = 'MWJ54SMBOGYZOKQPTV3ZDM3VUPROJXUDD5MK5BDWKKYSDTSS' # your Foursquare ID
CLIENT_SECRET = '5C1ZPNIZY2IQEL5CMN1AS22DED4EY0ILTZOTX40C4ACTE20Y' # your Foursquare Secret
VERSION = '20180605' # Foursquare API version
print('Your credentails:')
print('CLIENT_ID: ' + CLIENT_ID)
print('CLIENT_SECRET:' + CLIENT_SECRET)
Your credentails: CLIENT_ID: MWJ54SMBOGYZOKQPTV3ZDM3VUPROJXUDD5MK5BDWKKYSDTSS CLIENT_SECRET:5C1ZPNIZY2IQEL5CMN1AS22DED4EY0ILTZOTX40C4ACTE20Y
def getNearbyVenues(names, latitudes, longitudes, radius=1000, LIMIT=150):
venues_list=[]
for name, lat, lng in zip(names, latitudes, longitudes):
#print(name)
# create the API request URL
url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(
CLIENT_ID,
CLIENT_SECRET,
VERSION,
lat,
lng,
radius,
LIMIT)
# make the GET request
results = requests.get(url).json()["response"]['groups'][0]['items']
# return only relevant information for each nearby venue
venues_list.append([(
name,
lat,
lng,
v['venue']['name'],
v['venue']['location']['lat'],
v['venue']['location']['lng'],
v['venue']['categories'][0]['name']) for v in results])
nearby_venues = pd.DataFrame([item for venue_list in venues_list for item in venue_list])
nearby_venues.columns = ['Neighborhood',
'Neighborhood Latitude',
'Neighborhood Longitude',
'Venue',
'Venue Latitude',
'Venue Longitude',
'Venue Category']
return(nearby_venues)
boston_venues = getNearbyVenues(names=rents['Neighborhood'],
latitudes=rents['Lat'],
longitudes=rents['Lon'])
print(boston_venues.shape)
boston_venues.head()
(1726, 7)
| Neighborhood | Neighborhood Latitude | Neighborhood Longitude | Venue | Venue Latitude | Venue Longitude | Venue Category | |
|---|---|---|---|---|---|---|---|
| 0 | Roxbury | 42.324843 | -71.095016 | Crossfit On The Hill | 42.328008 | -71.098176 | Gym |
| 1 | Roxbury | 42.324843 | -71.095016 | Diablo Glass School | 42.328085 | -71.098262 | Art Gallery |
| 2 | Roxbury | 42.324843 | -71.095016 | Milkweed | 42.332168 | -71.099424 | New American Restaurant |
| 3 | Roxbury | 42.324843 | -71.095016 | Boston Building Resources | 42.328696 | -71.097894 | Furniture / Home Store |
| 4 | Roxbury | 42.324843 | -71.095016 | Lilly's Gourmet Pasta Express | 42.332445 | -71.100046 | Italian Restaurant |
boston_venues.groupby('Neighborhood').count()
| Neighborhood Latitude | Neighborhood Longitude | Venue | Venue Latitude | Venue Longitude | Venue Category | |
|---|---|---|---|---|---|---|
| Neighborhood | ||||||
| Allston | 87 | 87 | 87 | 87 | 87 | 87 |
| Back Bay | 100 | 100 | 100 | 100 | 100 | 100 |
| Bay Village | 100 | 100 | 100 | 100 | 100 | 100 |
| Beacon Hill | 100 | 100 | 100 | 100 | 100 | 100 |
| Brighton | 79 | 79 | 79 | 79 | 79 | 79 |
| Charlestown | 69 | 69 | 69 | 69 | 69 | 69 |
| Chinatown | 100 | 100 | 100 | 100 | 100 | 100 |
| Dorchester | 16 | 16 | 16 | 16 | 16 | 16 |
| Downtown | 100 | 100 | 100 | 100 | 100 | 100 |
| East Boston | 43 | 43 | 43 | 43 | 43 | 43 |
| Fenway | 100 | 100 | 100 | 100 | 100 | 100 |
| Hyde Park | 33 | 33 | 33 | 33 | 33 | 33 |
| Jamaica Plain | 58 | 58 | 58 | 58 | 58 | 58 |
| Kenmore | 2 | 2 | 2 | 2 | 2 | 2 |
| Longwood | 100 | 100 | 100 | 100 | 100 | 100 |
| Mattapan | 22 | 22 | 22 | 22 | 22 | 22 |
| Mission Hill | 70 | 70 | 70 | 70 | 70 | 70 |
| North End | 100 | 100 | 100 | 100 | 100 | 100 |
| Roslindale | 41 | 41 | 41 | 41 | 41 | 41 |
| Roxbury | 38 | 38 | 38 | 38 | 38 | 38 |
| South Boston | 73 | 73 | 73 | 73 | 73 | 73 |
| South Boston Waterfront | 100 | 100 | 100 | 100 | 100 | 100 |
| South End | 43 | 43 | 43 | 43 | 43 | 43 |
| West End | 100 | 100 | 100 | 100 | 100 | 100 |
| West Roxbury | 52 | 52 | 52 | 52 | 52 | 52 |
print('There are {} uniques categories.'.format(len(boston_venues['Venue Category'].unique())))
There are 242 uniques categories.
# one hot encoding
boston_onehot = pd.get_dummies(boston_venues[['Venue Category']], prefix="", prefix_sep="")
# add neighborhood column back to dataframe
boston_onehot['Neighborhood'] = boston_venues['Neighborhood']
# move neighborhood column to the first column
fixed_columns = [boston_onehot.columns[-1]] + list(boston_onehot.columns[:-1])
boston_onehot = boston_onehot[fixed_columns]
boston_onehot.head()
| Yoga Studio | ATM | Accessories Store | Afghan Restaurant | African Restaurant | American Restaurant | Antique Shop | Art Gallery | Art Museum | Arts & Crafts Store | Asian Restaurant | Athletics & Sports | Auditorium | Automotive Shop | BBQ Joint | Bagel Shop | Bakery | Bank | Bar | Baseball Field | Baseball Stadium | Basketball Court | Beach | Bed & Breakfast | Beer Bar | Beer Garden | Beer Store | Belgian Restaurant | Big Box Store | Bike Rental / Bike Share | Bistro | Board Shop | Boat or Ferry | Bookstore | Boutique | Bowling Alley | Boxing Gym | Brazilian Restaurant | Breakfast Spot | Brewery | Bubble Tea Shop | Building | Burger Joint | Burrito Place | Bus Station | Bus Stop | Business Service | Café | Caribbean Restaurant | Casino | Cemetery | Cheese Shop | Child Care Service | Chinese Restaurant | Chocolate Shop | Church | Circus | Climbing Gym | Clothing Store | Cocktail Bar | Coffee Shop | Comedy Club | Comfort Food Restaurant | Concert Hall | Construction & Landscaping | Convenience Store | Convention Center | Cosmetics Shop | Cuban Restaurant | Cupcake Shop | Cycle Studio | Deli / Bodega | Department Store | Dessert Shop | Diner | Discount Store | Dive Bar | Doctor's Office | Dog Run | Donut Shop | Dry Cleaner | Dumpling Restaurant | Electronics Store | Falafel Restaurant | Farm | Farmers Market | Fast Food Restaurant | Fish Market | Flower Shop | Food | Food & Drink Shop | Food Court | Food Service | Food Truck | French Restaurant | Fried Chicken Joint | Frozen Yogurt Shop | Furniture / Home Store | Garden | Gas Station | Gastropub | Gay Bar | Gift Shop | Golf Course | Gourmet Shop | Greek Restaurant | Grocery Store | Gym | Gym / Fitness Center | Gym Pool | Harbor / Marina | Hardware Store | Historic Site | History Museum | Hockey Arena | Hockey Field | Hockey Rink | Home Service | Hookah Bar | Hostel | Hot Dog Joint | Hotel | Hotel Bar | Hotpot Restaurant | Ice Cream Shop | Indian Restaurant | Insurance Office | Intersection | Irish Pub | Italian Restaurant | Japanese Restaurant | Jewelry Store | Juice Bar | Kids Store | Korean Restaurant | Lake | Latin American Restaurant | Laundromat | Lawyer | Library | Lingerie Store | Liquor Store | Locksmith | Lounge | Market | Mediterranean Restaurant | Men's Store | Metro Station | Mexican Restaurant | Middle Eastern Restaurant | Miscellaneous Shop | Mobile Phone Shop | Monument / Landmark | Moroccan Restaurant | Movie Theater | Museum | Music Store | Music Venue | Nail Salon | National Park | Neighborhood | New American Restaurant | Nightclub | Noodle House | Opera House | Optical Shop | Other Repair Shop | Outdoor Sculpture | Paper / Office Supplies Store | Park | Pastry Shop | Pedestrian Plaza | Performing Arts Venue | Pet Store | Pharmacy | Pizza Place | Planetarium | Platform | Playground | Plaza | Polish Restaurant | Pool | Pub | Record Shop | Recreation Center | Rental Car Location | Rental Service | Resort | Restaurant | River | Rock Club | Roof Deck | Salad Place | Salon / Barbershop | Sandwich Place | Scenic Lookout | Science Museum | Sculpture Garden | Seafood Restaurant | Shipping Store | Shoe Store | Shopping Mall | Skate Park | Skating Rink | Ski Chalet | Smoke Shop | Southern / Soul Food Restaurant | Souvenir Shop | Spa | Speakeasy | Sporting Goods Shop | Sports Bar | Steakhouse | Storage Facility | Street Food Gathering | Supermarket | Supplement Shop | Surf Spot | Sushi Restaurant | Taco Place | Tapas Restaurant | Tea Room | Tennis Court | Thai Restaurant | Theater | Thrift / Vintage Store | Tiki Bar | Tour Provider | Tourist Information Center | Track | Trail | Train Station | Tunnel | Vegetarian / Vegan Restaurant | Veterinarian | Video Store | Vietnamese Restaurant | Whisky Bar | Wine Bar | Wine Shop | Wings Joint | Women's Store | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | Roxbury | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | Roxbury | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | Roxbury | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | Roxbury | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | Roxbury | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
boston_grouped = boston_onehot.groupby('Neighborhood').mean().reset_index()
boston_grouped
| Neighborhood | Yoga Studio | ATM | Accessories Store | Afghan Restaurant | African Restaurant | American Restaurant | Antique Shop | Art Gallery | Art Museum | Arts & Crafts Store | Asian Restaurant | Athletics & Sports | Auditorium | Automotive Shop | BBQ Joint | Bagel Shop | Bakery | Bank | Bar | Baseball Field | Baseball Stadium | Basketball Court | Beach | Bed & Breakfast | Beer Bar | Beer Garden | Beer Store | Belgian Restaurant | Big Box Store | Bike Rental / Bike Share | Bistro | Board Shop | Boat or Ferry | Bookstore | Boutique | Bowling Alley | Boxing Gym | Brazilian Restaurant | Breakfast Spot | Brewery | Bubble Tea Shop | Building | Burger Joint | Burrito Place | Bus Station | Bus Stop | Business Service | Café | Caribbean Restaurant | Casino | Cemetery | Cheese Shop | Child Care Service | Chinese Restaurant | Chocolate Shop | Church | Circus | Climbing Gym | Clothing Store | Cocktail Bar | Coffee Shop | Comedy Club | Comfort Food Restaurant | Concert Hall | Construction & Landscaping | Convenience Store | Convention Center | Cosmetics Shop | Cuban Restaurant | Cupcake Shop | Cycle Studio | Deli / Bodega | Department Store | Dessert Shop | Diner | Discount Store | Dive Bar | Doctor's Office | Dog Run | Donut Shop | Dry Cleaner | Dumpling Restaurant | Electronics Store | Falafel Restaurant | Farm | Farmers Market | Fast Food Restaurant | Fish Market | Flower Shop | Food | Food & Drink Shop | Food Court | Food Service | Food Truck | French Restaurant | Fried Chicken Joint | Frozen Yogurt Shop | Furniture / Home Store | Garden | Gas Station | Gastropub | Gay Bar | Gift Shop | Golf Course | Gourmet Shop | Greek Restaurant | Grocery Store | Gym | Gym / Fitness Center | Gym Pool | Harbor / Marina | Hardware Store | Historic Site | History Museum | Hockey Arena | Hockey Field | Hockey Rink | Home Service | Hookah Bar | Hostel | Hot Dog Joint | Hotel | Hotel Bar | Hotpot Restaurant | Ice Cream Shop | Indian Restaurant | Insurance Office | Intersection | Irish Pub | Italian Restaurant | Japanese Restaurant | Jewelry Store | Juice Bar | Kids Store | Korean Restaurant | Lake | Latin American Restaurant | Laundromat | Lawyer | Library | Lingerie Store | Liquor Store | Locksmith | Lounge | Market | Mediterranean Restaurant | Men's Store | Metro Station | Mexican Restaurant | Middle Eastern Restaurant | Miscellaneous Shop | Mobile Phone Shop | Monument / Landmark | Moroccan Restaurant | Movie Theater | Museum | Music Store | Music Venue | Nail Salon | National Park | New American Restaurant | Nightclub | Noodle House | Opera House | Optical Shop | Other Repair Shop | Outdoor Sculpture | Paper / Office Supplies Store | Park | Pastry Shop | Pedestrian Plaza | Performing Arts Venue | Pet Store | Pharmacy | Pizza Place | Planetarium | Platform | Playground | Plaza | Polish Restaurant | Pool | Pub | Record Shop | Recreation Center | Rental Car Location | Rental Service | Resort | Restaurant | River | Rock Club | Roof Deck | Salad Place | Salon / Barbershop | Sandwich Place | Scenic Lookout | Science Museum | Sculpture Garden | Seafood Restaurant | Shipping Store | Shoe Store | Shopping Mall | Skate Park | Skating Rink | Ski Chalet | Smoke Shop | Southern / Soul Food Restaurant | Souvenir Shop | Spa | Speakeasy | Sporting Goods Shop | Sports Bar | Steakhouse | Storage Facility | Street Food Gathering | Supermarket | Supplement Shop | Surf Spot | Sushi Restaurant | Taco Place | Tapas Restaurant | Tea Room | Tennis Court | Thai Restaurant | Theater | Thrift / Vintage Store | Tiki Bar | Tour Provider | Tourist Information Center | Track | Trail | Train Station | Tunnel | Vegetarian / Vegan Restaurant | Veterinarian | Video Store | Vietnamese Restaurant | Whisky Bar | Wine Bar | Wine Shop | Wings Joint | Women's Store | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Allston | 0.011494 | 0.000000 | 0.000000 | 0.011494 | 0.000000 | 0.011494 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.011494 | 0.022989 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.045977 | 0.000000 | 0.034483 | 0.011494 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.00 | 0.011494 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.011494 | 0.011494 | 0.00 | 0.011494 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.011494 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.045977 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.011494 | 0.00 | 0.022989 | 0.00 | 0.011494 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.011494 | 0.000000 | 0.022989 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.011494 | 0.000000 | 0.011494 | 0.011494 | 0.011494 | 0.00 | 0.00000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.011494 | 0.011494 | 0.00 | 0.000000 | 0.00 | 0.011494 | 0.011494 | 0.011494 | 0.00 | 0.000000 | 0.022989 | 0.00 | 0.000000 | 0.0000 | 0.000000 | 0.000000 | 0.011494 | 0.000000 | 0.034483 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.011494 | 0.011494 | 0.000000 | 0.00 | 0.00 | 0.011494 | 0.000000 | 0.000000 | 0.011494 | 0.011494 | 0.011494 | 0.00 | 0.000000 | 0.00 | 0.011494 | 0.022989 | 0.00 | 0.000000 | 0.00 | 0.057471 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.022989 | 0.000000 | 0.000000 | 0.011494 | 0.011494 | 0.000000 | 0.000000 | 0.022989 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.057471 | 0.00 | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.022989 | 0.00 | 0.000000 | 0.000000 | 0.022989 | 0.00000 | 0.000000 | 0.000000 | 0.011494 | 0.000000 | 0.011494 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.011494 | 0.00 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.011494 | 0.000000 | 0.000000 | 0.022989 | 0.011494 | 0.00 | 0.011494 | 0.000000 | 0.022989 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.011494 | 0.000000 | 0.000000 | 0.022989 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 1 | Back Bay | 0.010000 | 0.000000 | 0.020000 | 0.000000 | 0.000000 | 0.040000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.010000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.010000 | 0.010000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.01 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.010000 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.000000 | 0.02 | 0.00 | 0.00 | 0.000000 | 0.020000 | 0.00 | 0.040000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.030000 | 0.000000 | 0.01 | 0.02000 | 0.010000 | 0.010000 | 0.020000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.010000 | 0.000000 | 0.00 | 0.01000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.01 | 0.000000 | 0.000000 | 0.010000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.0000 | 0.020000 | 0.010000 | 0.000000 | 0.030000 | 0.030000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.040000 | 0.000000 | 0.000000 | 0.040000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.040000 | 0.000000 | 0.01 | 0.020000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.02 | 0.000000 | 0.000000 | 0.000000 | 0.010000 | 0.010000 | 0.020000 | 0.000000 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.010000 | 0.00 | 0.000000 | 0.00 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.010000 | 0.00 | 0.00 | 0.00 | 0.010000 | 0.000000 | 0.000000 | 0.00 | 0.0000 | 0.000000 | 0.020000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.020000 | 0.000000 | 0.000000 | 0.00 | 0.020000 | 0.010000 | 0.010000 | 0.01000 | 0.000000 | 0.000000 | 0.040000 | 0.000000 | 0.010000 | 0.020000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.010000 | 0.00 | 0.030000 | 0.00 | 0.02 | 0.000000 | 0.02 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.010000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.010000 | 0.000000 | 0.000000 | 0.00 | 0.01 | 0.000000 | 0.000000 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.010000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.010000 |
| 2 | Bay Village | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.020000 | 0.00 | 0.000000 | 0.000000 | 0.01 | 0.040000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.020000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.00 | 0.000000 | 0.010000 | 0.010000 | 0.01 | 0.000000 | 0.00 | 0.000000 | 0.010000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.040000 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.010000 | 0.01 | 0.060000 | 0.01 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.01 | 0.02000 | 0.010000 | 0.000000 | 0.020000 | 0.000000 | 0.000000 | 0.010000 | 0.00 | 0.000000 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.01000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.02 | 0.000000 | 0.000000 | 0.000000 | 0.01 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.0000 | 0.010000 | 0.000000 | 0.010000 | 0.030000 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.01 | 0.000000 | 0.020000 | 0.000000 | 0.010000 | 0.000000 | 0.000000 | 0.01 | 0.000000 | 0.00 | 0.050000 | 0.010000 | 0.01 | 0.010000 | 0.00 | 0.010000 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.01 | 0.000000 | 0.000000 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.01 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.01 | 0.00 | 0.00 | 0.01 | 0.000000 | 0.020000 | 0.00 | 0.00 | 0.02 | 0.010000 | 0.000000 | 0.010000 | 0.00 | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.010000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.010000 | 0.040000 | 0.01000 | 0.000000 | 0.000000 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.060000 | 0.01 | 0.00 | 0.000000 | 0.02 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.010000 | 0.000000 | 0.000000 | 0.050000 | 0.000000 | 0.01 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.020000 | 0.00 | 0.010000 | 0.020000 | 0.000000 | 0.000000 |
| 3 | Beacon Hill | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.040000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.010000 | 0.010000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.020000 | 0.000000 | 0.020000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.01 | 0.00000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.010000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.010000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.010000 | 0.01 | 0.040000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.01000 | 0.010000 | 0.000000 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.020000 | 0.00 | 0.00000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.02 | 0.000000 | 0.000000 | 0.000000 | 0.01 | 0.000000 | 0.010000 | 0.00 | 0.010000 | 0.0000 | 0.010000 | 0.010000 | 0.010000 | 0.010000 | 0.030000 | 0.000000 | 0.000000 | 0.000000 | 0.030000 | 0.010000 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.040000 | 0.020000 | 0.010000 | 0.010000 | 0.000000 | 0.00 | 0.000000 | 0.01 | 0.060000 | 0.000000 | 0.00 | 0.010000 | 0.01 | 0.000000 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.010000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.020000 | 0.000000 | 0.000000 | 0.000000 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.010000 | 0.00 | 0.010000 | 0.000000 | 0.000000 | 0.020000 | 0.00 | 0.000000 | 0.01 | 0.01 | 0.01 | 0.01 | 0.000000 | 0.020000 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.040000 | 0.00 | 0.0000 | 0.010000 | 0.010000 | 0.000000 | 0.000000 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.020000 | 0.010000 | 0.000000 | 0.00 | 0.010000 | 0.000000 | 0.020000 | 0.01000 | 0.000000 | 0.000000 | 0.030000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.01 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.03 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.020000 | 0.000000 | 0.00 | 0.010000 | 0.000000 | 0.000000 | 0.010000 | 0.000000 | 0.00 | 0.00 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 4 | Brighton | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.012658 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.012658 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.037975 | 0.025316 | 0.025316 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.012658 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.012658 | 0.000000 | 0.000000 | 0.000000 | 0.037975 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.037975 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.050633 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.037975 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.025316 | 0.000000 | 0.012658 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.012658 | 0.037975 | 0.012658 | 0.000000 | 0.012658 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.012658 | 0.0000 | 0.000000 | 0.025316 | 0.037975 | 0.012658 | 0.012658 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.012658 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.012658 | 0.012658 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.012658 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.012658 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.012658 | 0.00 | 0.00 | 0.00 | 0.00 | 0.012658 | 0.012658 | 0.00 | 0.00 | 0.00 | 0.012658 | 0.025316 | 0.075949 | 0.00 | 0.0000 | 0.012658 | 0.000000 | 0.000000 | 0.000000 | 0.050633 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.012658 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.012658 | 0.025316 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.012658 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.012658 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.00 | 0.012658 | 0.00 | 0.000000 | 0.012658 | 0.000000 | 0.025316 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.025316 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.012658 | 0.000000 | 0.000000 | 0.012658 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.012658 | 0.012658 | 0.000000 |
| 5 | Charlestown | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.014493 | 0.00 | 0.014493 | 0.000000 | 0.00 | 0.000000 | 0.014493 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.014493 | 0.014493 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.014493 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.00 | 0.000000 | 0.028986 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.043478 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.028986 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.028986 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.014493 | 0.043478 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.014493 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.043478 | 0.00 | 0.000000 | 0.0000 | 0.000000 | 0.000000 | 0.043478 | 0.028986 | 0.000000 | 0.014493 | 0.014493 | 0.000000 | 0.000000 | 0.043478 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.014493 | 0.000000 | 0.000000 | 0.014493 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.014493 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.014493 | 0.000000 | 0.00 | 0.014493 | 0.000000 | 0.014493 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.014493 | 0.014493 | 0.00 | 0.000000 | 0.00 | 0.014493 | 0.000000 | 0.028986 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.057971 | 0.00 | 0.00 | 0.00 | 0.014493 | 0.014493 | 0.043478 | 0.00 | 0.0000 | 0.014493 | 0.014493 | 0.000000 | 0.014493 | 0.028986 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.014493 | 0.00000 | 0.000000 | 0.000000 | 0.014493 | 0.000000 | 0.000000 | 0.014493 | 0.014493 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.014493 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.014493 | 0.014493 | 0.014493 | 0.000000 | 0.00 | 0.00 | 0.014493 | 0.000000 | 0.000000 | 0.000000 | 0.014493 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.014493 | 0.000000 | 0.000000 | 0.000000 |
| 6 | Chinatown | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.070000 | 0.010000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.020000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.010000 | 0.00 | 0.000000 | 0.01 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.020000 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.040000 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.010000 | 0.01 | 0.060000 | 0.01 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.010000 | 0.000000 | 0.00 | 0.00000 | 0.010000 | 0.000000 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.02 | 0.000000 | 0.000000 | 0.000000 | 0.01 | 0.000000 | 0.020000 | 0.00 | 0.000000 | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.010000 | 0.020000 | 0.000000 | 0.000000 | 0.000000 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.01 | 0.000000 | 0.030000 | 0.010000 | 0.010000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.030000 | 0.010000 | 0.00 | 0.000000 | 0.00 | 0.010000 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.010000 | 0.00 | 0.010000 | 0.000000 | 0.010000 | 0.010000 | 0.010000 | 0.000000 | 0.000000 | 0.010000 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.01 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.020000 | 0.00 | 0.000000 | 0.01 | 0.00 | 0.00 | 0.02 | 0.000000 | 0.020000 | 0.00 | 0.00 | 0.02 | 0.000000 | 0.000000 | 0.020000 | 0.00 | 0.0000 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.010000 | 0.000000 | 0.000000 | 0.00 | 0.020000 | 0.010000 | 0.060000 | 0.01000 | 0.000000 | 0.000000 | 0.020000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.020000 | 0.00 | 0.00 | 0.000000 | 0.02 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.020000 | 0.000000 | 0.00 | 0.010000 | 0.000000 | 0.000000 | 0.030000 | 0.000000 | 0.00 | 0.00 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.010000 | 0.000000 | 0.000000 | 0.020000 | 0.00 | 0.010000 | 0.010000 | 0.000000 | 0.000000 |
| 7 | Dorchester | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.062500 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.062500 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.062500 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.062500 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.062500 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.0625 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.062500 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.062500 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.062500 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.125000 | 0.125000 | 0.00 | 0.0625 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.062500 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.062500 | 0.00 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 8 | Downtown | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.040000 | 0.01 | 0.000000 | 0.000000 | 0.00 | 0.010000 | 0.000000 | 0.00 | 0.000000 | 0.010000 | 0.000000 | 0.020000 | 0.010000 | 0.050000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.01 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.01 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.010000 | 0.010000 | 0.000000 | 0.01 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.060000 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.01 | 0.030000 | 0.00 | 0.000000 | 0.01 | 0.000000 | 0.010000 | 0.01 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.010000 | 0.000000 | 0.000000 | 0.010000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.050000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.01 | 0.00000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.010000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.010000 | 0.000000 | 0.01 | 0.010000 | 0.0000 | 0.010000 | 0.000000 | 0.010000 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.020000 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.01 | 0.00 | 0.010000 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.01 | 0.000000 | 0.00 | 0.030000 | 0.020000 | 0.00 | 0.000000 | 0.00 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.010000 | 0.000000 | 0.000000 | 0.030000 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.020000 | 0.01 | 0.000000 | 0.00 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.010000 | 0.00 | 0.01 | 0.00 | 0.000000 | 0.020000 | 0.030000 | 0.00 | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.010000 | 0.000000 | 0.000000 | 0.010000 | 0.000000 | 0.00 | 0.020000 | 0.000000 | 0.040000 | 0.00 | 0.000000 | 0.000000 | 0.060000 | 0.00000 | 0.000000 | 0.000000 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.01 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.010000 | 0.000000 | 0.01 | 0.000000 | 0.000000 | 0.010000 | 0.010000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.01 | 0.010000 | 0.000000 | 0.010000 | 0.000000 |
| 9 | East Boston | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.023256 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.023256 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.023256 | 0.00 | 0.00000 | 0.000000 | 0.00 | 0.000000 | 0.023256 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.023256 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.023256 | 0.000000 | 0.000000 | 0.000000 | 0.046512 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.023256 | 0.000000 | 0.023256 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.023256 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.023256 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.023256 | 0.00 | 0.000000 | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.023256 | 0.000000 | 0.000000 | 0.023256 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.023256 | 0.00 | 0.000000 | 0.00 | 0.069767 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.069767 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.046512 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.116279 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.093023 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.069767 | 0.00 | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.023256 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.023256 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.046512 | 0.00000 | 0.000000 | 0.000000 | 0.046512 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.023256 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 10 | Fenway | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.050000 | 0.00 | 0.020000 | 0.020000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.010000 | 0.000000 | 0.020000 | 0.000000 | 0.020000 | 0.030000 | 0.01 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.01000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.010000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.010000 | 0.000000 | 0.00 | 0.030000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.040000 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.010000 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.01 | 0.010000 | 0.00 | 0.000000 | 0.01 | 0.000000 | 0.000000 | 0.00 | 0.010000 | 0.000000 | 0.00 | 0.01000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.01 | 0.000000 | 0.020000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.030000 | 0.02 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.0000 | 0.000000 | 0.020000 | 0.010000 | 0.020000 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.030000 | 0.000000 | 0.000000 | 0.010000 | 0.010000 | 0.00 | 0.000000 | 0.00 | 0.010000 | 0.020000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.010000 | 0.000000 | 0.030000 | 0.000000 | 0.010000 | 0.000000 | 0.000000 | 0.020000 | 0.020000 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.01 | 0.010000 | 0.01 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.020000 | 0.00 | 0.01 | 0.00 | 0.000000 | 0.000000 | 0.030000 | 0.00 | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.030000 | 0.000000 | 0.000000 | 0.01 | 0.010000 | 0.000000 | 0.020000 | 0.00000 | 0.000000 | 0.000000 | 0.020000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.010000 | 0.000000 | 0.01 | 0.010000 | 0.00 | 0.01 | 0.010000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.030000 | 0.000000 | 0.000000 | 0.00 | 0.01 | 0.000000 | 0.000000 | 0.010000 | 0.000000 | 0.000000 | 0.010000 | 0.000000 | 0.000000 | 0.020000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 11 | Hyde Park | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.060606 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.030303 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.030303 | 0.030303 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.030303 | 0.000000 | 0.030303 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.030303 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.030303 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.030303 | 0.000000 | 0.00 | 0.000000 | 0.030303 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.030303 | 0.000000 | 0.00 | 0.000000 | 0.0000 | 0.000000 | 0.000000 | 0.090909 | 0.030303 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.060606 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.030303 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.030303 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.060606 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.060606 | 0.121212 | 0.00 | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.030303 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.030303 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.00 | 0.030303 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.030303 | 0.030303 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 12 | Jamaica Plain | 0.034483 | 0.000000 | 0.034483 | 0.000000 | 0.000000 | 0.034483 | 0.00 | 0.034483 | 0.000000 | 0.00 | 0.017241 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.068966 | 0.017241 | 0.017241 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.034483 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.017241 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.017241 | 0.000000 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.051724 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.017241 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.00 | 0.017241 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.017241 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.034483 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.017241 | 0.000000 | 0.017241 | 0.000000 | 0.017241 | 0.00 | 0.017241 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.017241 | 0.017241 | 0.017241 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.034483 | 0.00 | 0.017241 | 0.00 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.086207 | 0.00 | 0.00 | 0.00 | 0.017241 | 0.017241 | 0.051724 | 0.00 | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.034483 | 0.017241 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.017241 | 0.00000 | 0.000000 | 0.000000 | 0.017241 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.017241 | 0.00 | 0.000000 | 0.000000 | 0.017241 | 0.017241 | 0.034483 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.017241 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 13 | Kenmore | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.50 | 0.00000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.500000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 14 | Longwood | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.010000 | 0.040000 | 0.00 | 0.020000 | 0.030000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.010000 | 0.010000 | 0.010000 | 0.000000 | 0.010000 | 0.010000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.01000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.010000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.070000 | 0.010000 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.060000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.010000 | 0.00 | 0.010000 | 0.000000 | 0.00 | 0.01000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.01 | 0.000000 | 0.030000 | 0.000000 | 0.000000 | 0.000000 | 0.020000 | 0.00 | 0.00000 | 0.010000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.010000 | 0.030000 | 0.01 | 0.000000 | 0.010000 | 0.00 | 0.000000 | 0.0000 | 0.000000 | 0.030000 | 0.010000 | 0.030000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.020000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.020000 | 0.010000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.010000 | 0.000000 | 0.000000 | 0.030000 | 0.020000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.01 | 0.010000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.010000 | 0.00 | 0.000000 | 0.00 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.040000 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.060000 | 0.00 | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.030000 | 0.000000 | 0.000000 | 0.00 | 0.010000 | 0.000000 | 0.040000 | 0.00000 | 0.000000 | 0.000000 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.01 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.020000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.030000 | 0.010000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 15 | Mattapan | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.045455 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.045455 | 0.000000 | 0.045455 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.045455 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.090909 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.045455 | 0.00 | 0.000000 | 0.045455 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.045455 | 0.000000 | 0.000000 | 0.045455 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.045455 | 0.000000 | 0.000000 | 0.000000 | 0.045455 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.045455 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.045455 | 0.000000 | 0.000000 | 0.000000 | 0.045455 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.045455 | 0.045455 | 0.00 | 0.0000 | 0.045455 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.045455 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.045455 | 0.00 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.045455 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.045455 |
| 16 | Mission Hill | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.014286 | 0.014286 | 0.00 | 0.014286 | 0.042857 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.014286 | 0.000000 | 0.000000 | 0.014286 | 0.014286 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.014286 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.014286 | 0.014286 | 0.042857 | 0.014286 | 0.000000 | 0.071429 | 0.014286 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.014286 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.042857 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.014286 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.014286 | 0.085714 | 0.000000 | 0.000000 | 0.000000 | 0.028571 | 0.00 | 0.00000 | 0.014286 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.014286 | 0.00 | 0.000000 | 0.014286 | 0.014286 | 0.00 | 0.000000 | 0.014286 | 0.00 | 0.000000 | 0.0000 | 0.000000 | 0.014286 | 0.000000 | 0.028571 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.014286 | 0.000000 | 0.000000 | 0.014286 | 0.000000 | 0.00 | 0.014286 | 0.00 | 0.028571 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.014286 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.014286 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.014286 | 0.00 | 0.000000 | 0.00 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.042857 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.042857 | 0.00 | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.014286 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.014286 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.057143 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.014286 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.014286 | 0.000000 | 0.000000 | 0.028571 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.014286 | 0.000000 | 0.000000 | 0.000000 | 0.014286 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 17 | North End | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.010000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.050000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.01 | 0.00000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.030000 | 0.000000 | 0.000000 | 0.01 | 0.00000 | 0.000000 | 0.000000 | 0.01 | 0.01 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.020000 | 0.01 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.010000 | 0.000000 | 0.010000 | 0.010000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.01000 | 0.000000 | 0.01 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.010000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.010000 | 0.00 | 0.000000 | 0.0000 | 0.010000 | 0.000000 | 0.010000 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.030000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.340000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.020000 | 0.000000 | 0.000000 | 0.000000 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.010000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.00 | 0.00 | 0.01 | 0.000000 | 0.060000 | 0.01 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.050000 | 0.00 | 0.0000 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.01 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.010000 | 0.020000 | 0.00000 | 0.000000 | 0.000000 | 0.050000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.01 | 0.000000 | 0.000000 | 0.00 | 0.010000 | 0.00 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.010000 | 0.000000 | 0.000000 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.020000 | 0.000000 | 0.000000 |
| 18 | Roslindale | 0.048780 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.073171 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.024390 | 0.024390 | 0.048780 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.02439 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.02439 | 0.000000 | 0.024390 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.024390 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.02439 | 0.024390 | 0.000000 | 0.000000 | 0.000000 | 0.024390 | 0.000000 | 0.00 | 0.000000 | 0.024390 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.02439 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.0000 | 0.000000 | 0.024390 | 0.073171 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.024390 | 0.024390 | 0.00 | 0.000000 | 0.00 | 0.048780 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.024390 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.024390 | 0.097561 | 0.00 | 0.0000 | 0.000000 | 0.048780 | 0.000000 | 0.024390 | 0.024390 | 0.000000 | 0.000000 | 0.024390 | 0.000000 | 0.00 | 0.024390 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.048780 | 0.02439 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 19 | Roxbury | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.026316 | 0.000000 | 0.00 | 0.026316 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.026316 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.026316 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.026316 | 0.000000 | 0.000000 | 0.026316 | 0.000000 | 0.000000 | 0.026316 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.052632 | 0.00 | 0.000000 | 0.026316 | 0.00 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.026316 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.131579 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.026316 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.026316 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.026316 | 0.00 | 0.052632 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.026316 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.026316 | 0.000000 | 0.000000 | 0.000000 | 0.026316 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.026316 | 0.00 | 0.000000 | 0.00 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.026316 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.026316 | 0.078947 | 0.00 | 0.0000 | 0.000000 | 0.026316 | 0.000000 | 0.000000 | 0.000000 | 0.026316 | 0.026316 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.026316 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.052632 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.026316 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.026316 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 20 | South Boston | 0.000000 | 0.013699 | 0.000000 | 0.000000 | 0.000000 | 0.013699 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.013699 | 0.013699 | 0.068493 | 0.000000 | 0.00 | 0.000000 | 0.027397 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.013699 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.013699 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.013699 | 0.00 | 0.00 | 0.00 | 0.013699 | 0.000000 | 0.00 | 0.041096 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.027397 | 0.00 | 0.013699 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.000000 | 0.013699 | 0.013699 | 0.000000 | 0.027397 | 0.00 | 0.013699 | 0.054795 | 0.013699 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.013699 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.013699 | 0.000000 | 0.00 | 0.000000 | 0.0000 | 0.013699 | 0.000000 | 0.013699 | 0.041096 | 0.027397 | 0.000000 | 0.000000 | 0.000000 | 0.013699 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.013699 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.041096 | 0.000000 | 0.00 | 0.013699 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.013699 | 0.000000 | 0.000000 | 0.013699 | 0.000000 | 0.000000 | 0.000000 | 0.013699 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.013699 | 0.00 | 0.000000 | 0.00 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.027397 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.013699 | 0.082192 | 0.00 | 0.0000 | 0.000000 | 0.000000 | 0.013699 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.013699 | 0.013699 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.013699 | 0.000000 | 0.054795 | 0.00000 | 0.000000 | 0.000000 | 0.013699 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.00 | 0.027397 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.013699 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.013699 | 0.00 | 0.00 | 0.000000 | 0.013699 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 21 | South Boston Waterfront | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.010000 | 0.00 | 0.010000 | 0.020000 | 0.00 | 0.040000 | 0.000000 | 0.01 | 0.000000 | 0.010000 | 0.000000 | 0.020000 | 0.000000 | 0.030000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.010000 | 0.00 | 0.00000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.01 | 0.000000 | 0.000000 | 0.030000 | 0.000000 | 0.00 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.010000 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.01 | 0.040000 | 0.01 | 0.000000 | 0.00 | 0.000000 | 0.010000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.01000 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.01 | 0.010000 | 0.02 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.030000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.090000 | 0.010000 | 0.000000 | 0.010000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.060000 | 0.000000 | 0.00 | 0.010000 | 0.00 | 0.000000 | 0.000000 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.010000 | 0.000000 | 0.020000 | 0.000000 | 0.000000 | 0.030000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.01 | 0.010000 | 0.00 | 0.010000 | 0.000000 | 0.000000 | 0.020000 | 0.01 | 0.000000 | 0.00 | 0.01 | 0.00 | 0.00 | 0.000000 | 0.020000 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.010000 | 0.010000 | 0.00 | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.020000 | 0.000000 | 0.000000 | 0.00 | 0.020000 | 0.000000 | 0.010000 | 0.02000 | 0.000000 | 0.000000 | 0.080000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.01 | 0.00 | 0.000000 | 0.05 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.010000 | 0.010000 | 0.00 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 22 | South End | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.046512 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.023256 | 0.023256 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.046512 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.023256 | 0.000000 | 0.046512 | 0.00 | 0.00000 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.046512 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.023256 | 0.000000 | 0.000000 | 0.000000 | 0.023256 | 0.000000 | 0.00 | 0.000000 | 0.046512 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.023256 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.046512 | 0.000000 | 0.00 | 0.000000 | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.023256 | 0.023256 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.046512 | 0.000000 | 0.000000 | 0.023256 | 0.00 | 0.00 | 0.000000 | 0.046512 | 0.023256 | 0.000000 | 0.023256 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.023256 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.023256 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.023256 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.069767 | 0.00 | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.023256 | 0.000000 | 0.000000 | 0.023256 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.046512 | 0.00000 | 0.023256 | 0.023256 | 0.000000 | 0.046512 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.00 | 0.046512 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 23 | West End | 0.020000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.010000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.070000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.01 | 0.00000 | 0.000000 | 0.00 | 0.000000 | 0.010000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.010000 | 0.000000 | 0.00 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.010000 | 0.000000 | 0.000000 | 0.01 | 0.00000 | 0.000000 | 0.000000 | 0.01 | 0.01 | 0.01 | 0.000000 | 0.000000 | 0.00 | 0.040000 | 0.01 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.01000 | 0.020000 | 0.000000 | 0.000000 | 0.010000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.01000 | 0.000000 | 0.01 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.010000 | 0.02 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.010000 | 0.00 | 0.000000 | 0.0000 | 0.020000 | 0.010000 | 0.010000 | 0.000000 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.030000 | 0.010000 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.030000 | 0.020000 | 0.010000 | 0.010000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.080000 | 0.000000 | 0.00 | 0.010000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.010000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.020000 | 0.000000 | 0.000000 | 0.000000 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.010000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.01 | 0.01 | 0.01 | 0.000000 | 0.050000 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.050000 | 0.01 | 0.0000 | 0.000000 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.020000 | 0.000000 | 0.000000 | 0.00 | 0.010000 | 0.010000 | 0.020000 | 0.00000 | 0.020000 | 0.000000 | 0.040000 | 0.000000 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.01 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.00 | 0.010000 | 0.01 | 0.000000 | 0.01 | 0.000000 | 0.000000 | 0.000000 | 0.010000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 24 | West Roxbury | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.038462 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.019231 | 0.019231 | 0.019231 | 0.019231 | 0.038462 | 0.019231 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.019231 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.019231 | 0.00 | 0.019231 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.057692 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.00000 | 0.019231 | 0.000000 | 0.000000 | 0.019231 | 0.019231 | 0.000000 | 0.00 | 0.000000 | 0.019231 | 0.000000 | 0.000000 | 0.019231 | 0.000000 | 0.00 | 0.00000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.019231 | 0.000000 | 0.00 | 0.000000 | 0.0000 | 0.000000 | 0.000000 | 0.038462 | 0.019231 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.019231 | 0.00 | 0.000000 | 0.00 | 0.019231 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.057692 | 0.019231 | 0.000000 | 0.000000 | 0.019231 | 0.000000 | 0.000000 | 0.019231 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.019231 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.038462 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.076923 | 0.076923 | 0.00 | 0.0000 | 0.000000 | 0.019231 | 0.000000 | 0.000000 | 0.019231 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.019231 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.00 | 0.019231 | 0.00 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.019231 | 0.000000 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.019231 | 0.000000 | 0.000000 | 0.019231 | 0.019231 | 0.019231 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
num_top_venues = 5
for hood in boston_grouped['Neighborhood']:
print("----"+hood+"----")
temp = boston_grouped[boston_grouped['Neighborhood'] == hood].T.reset_index()
temp.columns = ['venue','freq']
temp = temp.iloc[1:]
temp['freq'] = temp['freq'].astype(float)
temp = temp.round({'freq': 2})
print(temp.sort_values('freq', ascending=False).reset_index(drop=True).head(num_top_venues))
print('\n')
----Allston----
venue freq
0 Pizza Place 0.06
1 Korean Restaurant 0.06
2 Chinese Restaurant 0.05
3 Bakery 0.05
4 Bar 0.03
----Back Bay----
venue freq
0 Italian Restaurant 0.04
1 Hotel 0.04
2 Coffee Shop 0.04
3 Ice Cream Shop 0.04
4 American Restaurant 0.04
----Bay Village----
venue freq
0 Coffee Shop 0.06
1 Spa 0.06
2 Theater 0.05
3 Italian Restaurant 0.05
4 Chinese Restaurant 0.04
----Beacon Hill----
venue freq
0 Italian Restaurant 0.06
1 American Restaurant 0.04
2 Hotel 0.04
3 Pizza Place 0.04
4 Coffee Shop 0.04
----Brighton----
venue freq
0 Pizza Place 0.08
1 Coffee Shop 0.05
2 Pub 0.05
3 Grocery Store 0.04
4 Donut Shop 0.04
----Charlestown----
venue freq
0 Park 0.06
1 Grocery Store 0.04
2 History Museum 0.04
3 Donut Shop 0.04
4 Café 0.04
----Chinatown----
venue freq
0 Asian Restaurant 0.07
1 Sandwich Place 0.06
2 Coffee Shop 0.06
3 Chinese Restaurant 0.04
4 Italian Restaurant 0.03
----Dorchester----
venue freq
0 Pizza Place 0.12
1 Pharmacy 0.12
2 Bank 0.06
3 Golf Course 0.06
4 Caribbean Restaurant 0.06
----Downtown----
venue freq
0 Café 0.06
1 Sandwich Place 0.06
2 Donut Shop 0.05
3 Bar 0.05
4 Rock Club 0.04
----East Boston----
venue freq
0 Mexican Restaurant 0.12
1 Park 0.09
2 Italian Restaurant 0.07
3 Pizza Place 0.07
4 Latin American Restaurant 0.07
----Fenway----
venue freq
0 American Restaurant 0.05
1 Café 0.04
2 Pizza Place 0.03
3 Lounge 0.03
4 Furniture / Home Store 0.03
----Hyde Park----
venue freq
0 Pizza Place 0.12
1 Grocery Store 0.09
2 Pharmacy 0.06
3 Home Service 0.06
4 American Restaurant 0.06
----Jamaica Plain----
venue freq
0 Park 0.09
1 Bakery 0.07
2 Coffee Shop 0.05
3 Pizza Place 0.05
4 Bookstore 0.03
----Kenmore----
venue freq
0 Farm 0.5
1 Liquor Store 0.5
2 Yoga Studio 0.0
3 Monument / Landmark 0.0
4 Movie Theater 0.0
----Longwood----
venue freq
0 Café 0.07
1 Pizza Place 0.06
2 Coffee Shop 0.06
3 Park 0.04
4 American Restaurant 0.04
----Mattapan----
venue freq
0 Donut Shop 0.09
1 Metro Station 0.05
2 Fast Food Restaurant 0.05
3 Food 0.05
4 Gym / Fitness Center 0.05
----Mission Hill----
venue freq
0 Donut Shop 0.09
1 Café 0.07
2 Sandwich Place 0.06
3 Coffee Shop 0.04
4 Bus Station 0.04
----North End----
venue freq
0 Italian Restaurant 0.34
1 Park 0.06
2 Seafood Restaurant 0.05
3 Bakery 0.05
4 Pizza Place 0.05
----Roslindale----
venue freq
0 Pizza Place 0.10
1 Grocery Store 0.07
2 American Restaurant 0.07
3 Yoga Studio 0.05
4 Italian Restaurant 0.05
----Roxbury----
venue freq
0 Donut Shop 0.13
1 Pizza Place 0.08
2 Convenience Store 0.05
3 Italian Restaurant 0.05
4 Skating Rink 0.05
----South Boston----
venue freq
0 Pizza Place 0.08
1 Bar 0.07
2 Donut Shop 0.05
3 Sandwich Place 0.05
4 Gym 0.04
----South Boston Waterfront----
venue freq
0 Hotel 0.09
1 Seafood Restaurant 0.08
2 Italian Restaurant 0.06
3 Steakhouse 0.05
4 Coffee Shop 0.04
----South End----
venue freq
0 Pizza Place 0.07
1 Casino 0.05
2 Donut Shop 0.05
3 Burger Joint 0.05
4 Sandwich Place 0.05
----West End----
venue freq
0 Italian Restaurant 0.08
1 Bakery 0.07
2 Pizza Place 0.05
3 Park 0.05
4 Seafood Restaurant 0.04
----West Roxbury----
venue freq
0 Pizza Place 0.08
1 Pharmacy 0.08
2 Convenience Store 0.06
3 Liquor Store 0.06
4 American Restaurant 0.04
def return_most_common_venues(row, num_top_venues):
row_categories = row.iloc[1:]
row_categories_sorted = row_categories.sort_values(ascending=False)
return row_categories_sorted.index.values[0:num_top_venues]
num_top_venues = 10
indicators = ['st', 'nd', 'rd']
# create columns according to number of top venues
columns = ['Neighborhood']
for ind in np.arange(num_top_venues):
try:
columns.append('{}{} Most Common Venue'.format(ind+1, indicators[ind]))
except:
columns.append('{}th Most Common Venue'.format(ind+1))
# create a new dataframe
neighborhoods_venues_sorted = pd.DataFrame(columns=columns)
neighborhoods_venues_sorted['Neighborhood'] = boston_grouped['Neighborhood']
for ind in np.arange(boston_grouped.shape[0]):
neighborhoods_venues_sorted.iloc[ind, 1:] = return_most_common_venues(boston_grouped.iloc[ind, :], num_top_venues)
neighborhoods_venues_sorted
| Neighborhood | 1st Most Common Venue | 2nd Most Common Venue | 3rd Most Common Venue | 4th Most Common Venue | 5th Most Common Venue | 6th Most Common Venue | 7th Most Common Venue | 8th Most Common Venue | 9th Most Common Venue | 10th Most Common Venue | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Allston | Pizza Place | Korean Restaurant | Chinese Restaurant | Bakery | Bar | Gym / Fitness Center | Thai Restaurant | Coffee Shop | Sushi Restaurant | Rock Club |
| 1 | Back Bay | Italian Restaurant | American Restaurant | Seafood Restaurant | Hotel | Coffee Shop | Ice Cream Shop | Spa | Gym / Fitness Center | Cosmetics Shop | Gym |
| 2 | Bay Village | Coffee Shop | Spa | Italian Restaurant | Theater | Sandwich Place | Asian Restaurant | Chinese Restaurant | Gym | Park | Cycle Studio |
| 3 | Beacon Hill | Italian Restaurant | Hotel | Pizza Place | American Restaurant | Coffee Shop | Historic Site | Seafood Restaurant | Steakhouse | Gym / Fitness Center | Bakery |
| 4 | Brighton | Pizza Place | Coffee Shop | Pub | Grocery Store | Donut Shop | Convenience Store | Chinese Restaurant | Café | Bakery | Thai Restaurant |
| 5 | Charlestown | Park | Café | Gastropub | Grocery Store | Donut Shop | History Museum | Pizza Place | National Park | Coffee Shop | Gym |
| 6 | Chinatown | Asian Restaurant | Coffee Shop | Sandwich Place | Chinese Restaurant | Italian Restaurant | Theater | Hotel | New American Restaurant | Gastropub | Seafood Restaurant |
| 7 | Dorchester | Pizza Place | Pharmacy | Fried Chicken Joint | Mobile Phone Shop | Platform | Fast Food Restaurant | Discount Store | Park | Market | Sandwich Place |
| 8 | Downtown | Café | Sandwich Place | Bar | Donut Shop | American Restaurant | Rock Club | Italian Restaurant | Pizza Place | Mexican Restaurant | Coffee Shop |
| 9 | East Boston | Mexican Restaurant | Park | Italian Restaurant | Latin American Restaurant | Pizza Place | Café | Liquor Store | Sandwich Place | Seafood Restaurant | Burrito Place |
| 10 | Fenway | American Restaurant | Café | Thai Restaurant | Furniture / Home Store | Restaurant | Hotel | Burger Joint | Pizza Place | Baseball Field | Lounge |
| 11 | Hyde Park | Pizza Place | Grocery Store | Park | Home Service | Pharmacy | American Restaurant | Bar | Theater | Storage Facility | Business Service |
| 12 | Jamaica Plain | Park | Bakery | Pizza Place | Coffee Shop | Thrift / Vintage Store | Ice Cream Shop | New American Restaurant | Pub | Bookstore | Yoga Studio |
| 13 | Kenmore | Farm | Liquor Store | Women's Store | Food | Fish Market | Fast Food Restaurant | Farmers Market | Falafel Restaurant | Electronics Store | Dumpling Restaurant |
| 14 | Longwood | Café | Pizza Place | Coffee Shop | American Restaurant | Park | Sandwich Place | Thai Restaurant | Greek Restaurant | Furniture / Home Store | Donut Shop |
| 15 | Mattapan | Donut Shop | Women's Store | Hardware Store | Playground | Pizza Place | Pharmacy | Mobile Phone Shop | Metro Station | Liquor Store | Ice Cream Shop |
| 16 | Mission Hill | Donut Shop | Café | Sandwich Place | Pizza Place | Coffee Shop | Art Museum | Park | Bus Station | Gym | Italian Restaurant |
| 17 | North End | Italian Restaurant | Park | Pizza Place | Bakery | Seafood Restaurant | Café | Historic Site | Sandwich Place | Market | Coffee Shop |
| 18 | Roslindale | Pizza Place | American Restaurant | Grocery Store | Yoga Studio | Plaza | Italian Restaurant | Bar | Sandwich Place | Discount Store | Liquor Store |
| 19 | Roxbury | Donut Shop | Pizza Place | Skating Rink | Convenience Store | Italian Restaurant | Burger Joint | Sandwich Place | Park | Liquor Store | Mobile Phone Shop |
| 20 | South Boston | Pizza Place | Bar | Donut Shop | Sandwich Place | Coffee Shop | Gym | Italian Restaurant | Gym / Fitness Center | Park | Dive Bar |
| 21 | South Boston Waterfront | Hotel | Seafood Restaurant | Italian Restaurant | Steakhouse | Coffee Shop | Asian Restaurant | Mexican Restaurant | Gym | Brewery | Bar |
| 22 | South End | Pizza Place | Shipping Store | Donut Shop | Casino | Sports Bar | Burger Joint | Sandwich Place | Hotel | American Restaurant | Coffee Shop |
| 23 | West End | Italian Restaurant | Bakery | Pizza Place | Park | Seafood Restaurant | Coffee Shop | Historic Site | Hotel | Yoga Studio | Science Museum |
| 24 | West Roxbury | Pizza Place | Pharmacy | Convenience Store | Liquor Store | Bank | Grocery Store | American Restaurant | Park | Spa | Nail Salon |
# set number of clusters
kclusters = 7
boston_grouped_clustering = boston_grouped.drop('Neighborhood', 1)
# run k-means clustering
kmeans = KMeans(n_clusters=kclusters, random_state=0).fit(boston_grouped_clustering)
#check cluster labels generated for each row in the dataframe
kmeans.labels_[0:10]
array([0, 1, 1, 1, 0, 0, 1, 6, 5, 5], dtype=int32)
# add clustering labels
neighborhoods_venues_sorted.insert(0, 'Cluster Labels', kmeans.labels_)
boston_merged = rents
# merge toronto_grouped with toronto_data to add latitude/longitude for each neighborhood
boston_merged = boston_merged.join(neighborhoods_venues_sorted.set_index('Neighborhood'), on='Neighborhood')
boston_merged.head() # check the last columns!
| Neighborhood | Median price per sqft | Lat | Lon | Cluster Labels | 1st Most Common Venue | 2nd Most Common Venue | 3rd Most Common Venue | 4th Most Common Venue | 5th Most Common Venue | 6th Most Common Venue | 7th Most Common Venue | 8th Most Common Venue | 9th Most Common Venue | 10th Most Common Venue | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Roxbury | 1.914 | 42.324843 | -71.095016 | 5 | Donut Shop | Pizza Place | Skating Rink | Convenience Store | Italian Restaurant | Burger Joint | Sandwich Place | Park | Liquor Store | Mobile Phone Shop |
| 1 | Jamaica Plain | 2.360 | 42.309820 | -71.120330 | 1 | Park | Bakery | Pizza Place | Coffee Shop | Thrift / Vintage Store | Ice Cream Shop | New American Restaurant | Pub | Bookstore | Yoga Studio |
| 2 | East Boston | 2.442 | 42.375097 | -71.039217 | 5 | Mexican Restaurant | Park | Italian Restaurant | Latin American Restaurant | Pizza Place | Café | Liquor Store | Sandwich Place | Seafood Restaurant | Burrito Place |
| 3 | Brighton | 2.896 | 42.350097 | -71.156442 | 0 | Pizza Place | Coffee Shop | Pub | Grocery Store | Donut Shop | Convenience Store | Chinese Restaurant | Café | Bakery | Thai Restaurant |
| 4 | Mattapan | 1.896 | 42.272321 | -71.086995 | 3 | Donut Shop | Women's Store | Hardware Store | Playground | Pizza Place | Pharmacy | Mobile Phone Shop | Metro Station | Liquor Store | Ice Cream Shop |
boston_map = folium.Map(location=[42.3602534, -71.0582912], zoom_start=12)
boston_map.choropleth(
geo_data=boston_data,
data=rents,
columns=['Neighborhood', 'Median price per sqft'],
key_on='feature.properties.Name',
fill_color='YlOrBr',
fill_opacity=0.7,
line_opacity=0.2,
legend_name='Housing Prices per sqft',
highlight = True,
reset=True
)
# set color scheme for the clusters
x = np.arange(kclusters)
ys = [i + x + (i*x)**2 for i in range(kclusters)]
colors_array = cm.rainbow(np.linspace(0, 1, len(ys)))
rainbow = [colors.rgb2hex(i) for i in colors_array]
# add markers to the map
markers_colors = []
for lat, lon, neigh, price, venue, cluster in zip(boston_merged['Lat'], boston_merged['Lon'], boston_merged['Neighborhood'], boston_merged['Median price per sqft'], boston_merged['1st Most Common Venue'], boston_merged['Cluster Labels']):
label = folium.Popup(str(neigh)+'\n'+
'Cluster:'+
str(cluster)+'\n'+
'Avg Price:'+
str(format(price,'.2f'))+'\n'+
'Venue:'+
str(venue)+'\n',
parse_html = True,
max_width = '200%')
folium.CircleMarker(
[lat, lon],
radius=6,
popup=label,
color=rainbow[cluster-1],
fill=True,
fill_color=rainbow[cluster-1],
fill_opacity=0.7).add_to(boston_map)
boston_map
/usr/local/lib/python3.6/dist-packages/folium/folium.py:426: FutureWarning: The choropleth method has been deprecated. Instead use the new Choropleth class, which has the same arguments. See the example notebook 'GeoJSON_and_choropleth' for how to do this. FutureWarning
def top3(cluster):
temp = cluster.iloc[:,1:4].apply(pd.value_counts)
temp = temp.sum(axis = 1).sort_values(ascending = False)
venue = temp.index[0:3]
count = temp.values[0:3]
count = [int(i) for i in count]
for i in range(3):
label = str(count[i])+ " "+ venue[i]
print(label)
return venue,count
cluster1 = boston_merged.loc[boston_merged['Cluster Labels'] == 0, boston_merged.columns[[1] + list(range(5, boston_merged.shape[1]))]].reset_index(drop=True)
a,b = top3(cluster1)
6 Pizza Place 2 Park 2 Grocery Store
cluster2 = boston_merged.loc[boston_merged['Cluster Labels'] == 1, boston_merged.columns[[1] + list(range(5, boston_merged.shape[1]))]].reset_index(drop=True)
a,b = top3(cluster2)
5 Italian Restaurant 3 Pizza Place 2 Seafood Restaurant
cluster3 = boston_merged.loc[boston_merged['Cluster Labels'] == 2, boston_merged.columns[[1] + list(range(5, boston_merged.shape[1]))]].reset_index(drop=True)
a,b = top3(cluster3)
1 Women's Store 1 Liquor Store 1 Farm
cluster4 = boston_merged.loc[boston_merged['Cluster Labels'] == 3, boston_merged.columns[[1] + list(range(5, boston_merged.shape[1]))]].reset_index(drop=True)
a,b = top3(cluster4)
1 Women's Store 1 Hardware Store 1 Donut Shop
cluster5 = boston_merged.loc[boston_merged['Cluster Labels'] == 4, boston_merged.columns[[1] + list(range(5, boston_merged.shape[1]))]].reset_index(drop=True)
a,b = top3(cluster5)
1 Pizza Place 1 Park 1 Italian Restaurant
cluster5 = boston_merged.loc[boston_merged['Cluster Labels'] == 5, boston_merged.columns[[1] + list(range(5, boston_merged.shape[1]))]].reset_index(drop=True)
a,b = top3(cluster5)
4 Café 3 Pizza Place 3 Donut Shop
cluster6 = boston_merged.loc[boston_merged['Cluster Labels'] == 6, boston_merged.columns[[1] + list(range(5, boston_merged.shape[1]))]].reset_index(drop=True)
a,b = top3(cluster6)
1 Pizza Place 1 Pharmacy 1 Fried Chicken Joint
As per our analysis we can see the southeren region of Boston have the highest number of cluster centers. This indicates greater chances of prosperous business. However, there is also a tradeoff that comes in terms of high rent. In addition, there are greater occurences of crimes in that region. In a nutshell, there is plenty of information available for law enforcement agencies, real-estate agents and ctiy councils to improve standards in the city